I'm working in a call stack of variable depth that looks like
TopLevelFunction
-> <SomeOtherFunction(s), 1 or more>
-> AssignmentFunction
Now, my goal is to assign a variable created in AssignmentFunction, to the environment of TopLevelFunction. I know I can extract the stack with sys.calls, so my current approach is
# get the call stack and search for TopLevelFunction
depth <- which(stringr::str_detect(as.character(sys.calls()), "TopLevelFunction"))
# assign in TopLevelFunction's environment
assign(varName, varValue, envir = sys.frame(depth))
I'm more or less fine with that, though I am not sure if that's a good idea to convert call objects to character vectors. Is that approach error-prone? More generally, how would you search for a specific parent environment, knowing only the name of the function?
A fn like this
get_toplevel_env <- function(env) {
if (identical(parent.env(env), globalenv())) {
env
} else {
get_toplevel_env(parent.env(env))
}
}
And use it within any level of your nested-functions like this?
get_toplevel_env(as.environment(-1))
I'm not sure if I understood correctly what you want to do, but, woulnd't it work to use parent.env(as.environment(-1))?
In this example it seems to work.
fn1 <- function() {
fn1.1 <- function(){
assign("parentvar", "PARENT",
envir = parent.env(as.environment(-1)))
}
fn1.1()
print(parentvar)
}
fn1()
Maybe other possibility is to use <<-, which assigns in the global environment, I think. But maybe that's not what you want.
Related
I have developed the following two functions:
save_sysdata <- function(...) {
data <- eval(substitute(alist(...)))
data <- purrr::map_chr(data, add_dot)
save(list = data, file = "sysdata.rda", compress = "bzip2", version = 2)
}
add_dot <- function(object) {
object <- object # Why is this required?
name <- paste0(".", deparse(substitute(object)))
# parent.frame(3) because evaluating in global (or caller function); 2 because assigning in save_sysdata.
assign(name, eval(object, envir = parent.frame(3)), envir = parent.frame(2))
return(name)
}
The purpose of this set of functions is to provide an object (x) and save it as a sysdata.rda file but as a hidden object. This requires adding a . to the object symbol (.x).
The set of functions as I have it works and accomplishes what I want. However, it requires a bit of code that I don't understand why it works or what it's doing. I'm not even sure how I came up with this particular line as a solution.
If I remove the line object <- object from the add_dot function, the whole thing fails to work. It actually just generates an empty sysdata.rda file.
Can anyone explain why this line is necessary and what it is doing?
And if you have a more efficient way of accomplishing this, please let me know. It was a fun exercise to figure this out myself but I'm sure there is a better way.
For a reprex, simply copy the above functions and run:
x <- "test"
save_sysdata(x)
Then load the sysdata.rda file into your global environment and type .x. You should return [1] "test".
Here's an alternative version
save_sysdata <- function(...) {
pnames <- sapply(match.call(expand.dots=FALSE)$..., deparse)
snames <- paste0(".", pnames)
senv <- setNames(list(...), snames)
save(list = snames, envir=list2env(senv), file = "sysdata.rda", compress = "bzip2", version = 2)
}
We dump the values into a named list and granbing the names of the parameter with match.call(). We add dots to the names and then turn that list into an environment that we can use with save.
The reason your version required object <- object is that function parameters are lazily evaluated. Since you never actually use the value of that object in your function without the assignment, it remains a promise and is never added tot he function environment. Sometimes you'll see force(object) instead which does the same thing.
I read some post where Hadley made a joke about a self destructing function. I thought this would be relatively simple to implement but turns out it's not.
Here is an attempt to write a function named self_delete that is a quine and attempts to self destruct after printing its body. The idea was to search for the function's name in .GlobalEnv and delete it but that doesn't work. I would like to understand why this is the case.
self_delete<- function(){
print(body(self_delete))
rm(list=lsf.str(pattern="self_delete"))
}
Calling the above prints the following as expected but does not delete itself from .Globalenv, what am I missing? I did try with rm and ls too with no luck
self_delete()
{
print(body(self_delete))
rm(list = lsf.str(pattern = "self_delete"))
}
You forgot to set the envir argument to rm(), so it's trying to delete self_delete from the calling frame, not from globalenv().
This works:
self_delete <- function(){
print(body(self_delete))
rm("self_delete", envir = globalenv())
}
I have a package which provides a script and some functions. Within the script I assign a variable which will be used by the function. This works if the function gets executed within the script but might fail if I just call the function since the variable doesn't exist.
If I use devtools::check() I get warnings, that the variable within the function isn't defined. How can I handle this properly?
Edit
I am thinking about to use get() within the function to assign the variable within the function to get rid of this warnings. So the question is, is myp2 the correct way of doing something like this? Maybe some trycatch to handle errors?
ab <- c(1,2,3)
myp1 <- function() {
print(ab)
return(1)
}
myp2 <- function() {
ab <- get('ab')
print(ab)
return(1)
}
myp1()
myp2()
You could do something like
if(!exists("your variable")){
stop("You have not defined your variable")}
This would check to see if what you are looking for exists. A better practice would be to define the variable in the function and have the default value be the name of the thing for which you are looking.
myp <- function(x) {
print(x)
return(1)
}
ab <- c(1,2,3)
myp(x = ab)
If possible, it would be also better to substitute the script with a function.
I am not entirely sure I understand what substitute does, although I've used in it code before. Today I encountered in shiny::exprToFunction the following lines of code:
function (expr, env = parent.frame(2), quoted = FALSE, caller_offset = 1)
{
expr_sub <- eval(substitute(substitute(expr)),
...
}
Can someone please explain why nested substitute is used here? A easy to run example would really help.
Take a look at
a<-function(aa) {
b(aa)
}
b<-function(bb) {
z(bb)
}
z<-function(zz) {
print(substitute(zz))
print(substitute(substitute(zz)))
print(eval(substitute(substitute(zz)), parent.frame()))
}
q<-5
a(q)
# bb
# substitute(bb)
# aa
The first/inner substitute grabs the name/symbol that was passed to the called function. The second/outer substitute() simply wraps a substitute() command around that discovered name/symbol. Then that substitute() is evaluated in the parent environment where it came from.
The method of using substitute to capture variable names only works when parameters are still promises; that is, they have not yet been evaluated.
Is there an R function that lists all the functions in an R script file along with their arguments?
i.e. an output of the form:
func1(var1, var2)
func2(var4, var10)
.
.
.
func10(varA, varB)
Using [sys.]source has the very undesirable side-effect of executing the source inside the file. At the worst this has security problems, but even “benign” code may simply have unintended side-effects when executed. At best it just takes unnecessary time (and potentially a lot).
It’s actually unnecessary to execute the code, though: it is enough to parse it, and then do some syntactical analysis.
The actual code is trivial:
file_parsed = parse(filename)
functions = Filter(is_function, file_parsed)
function_names = unlist(Map(function_name, functions))
And there you go, function_names contains a vector of function names. Extending this to also list the function arguments is left as an exercise to the reader. Hint: there are two approaches. One is to eval the function definition (now that we know it’s a function definition, this is safe); the other is to “cheat” and just get the list of arguments to the function call.
The implementation of the functions used above is also not particularly hard. There’s probably even something already in R core packages (‘utils’ has a lot of stuff) but since I’m not very familiar with this, I’ve just written them myself:
is_function = function (expr) {
if (! is_assign(expr)) return(FALSE)
value = expr[[3L]]
is.call(value) && as.character(value[[1L]]) == 'function'
}
function_name = function (expr) {
as.character(expr[[2L]])
}
is_assign = function (expr) {
is.call(expr) && as.character(expr[[1L]]) %in% c('=', '<-', 'assign')
}
This correctly recognises function declarations of the forms
f = function (…) …
f <- function (…) …
assign('f', function (…) …)
It won’t work for more complex code, since assignments can be arbitrarily complex and in general are only resolvable by actually executing the code. However, the three forms above probably account for ≫ 99% of all named function definitions in practice.
UPDATE: Please refer to the answer by #Konrad Rudolph instead
You can create a new environment, source your file in that environment and then list the functions in it using lsf.str() e.g.
test.env <- new.env()
sys.source("myfile.R", envir = test.env)
lsf.str(envir=test.env)
rm(test.env)
or if you want to wrap it as a function:
listFunctions <- function(filename) {
temp.env <- new.env()
sys.source(filename, envir = temp.env)
functions <- lsf.str(envir=temp.env)
rm(temp.env)
return(functions)
}